Multi-Task Learning approach to identify sentences with impact and affected location in a disaster news report

The first priority of action in the Sendai Framework for Disaster Risk Reduction 2015-2030 advocates the understanding of disaster risk by collecting and processing practical information related to disasters.A smart collection may be the compilation of relevant and summarized news articles focused on some key pieces of information such as disaster event type, geographic location(s), and impacts.In this article, a Multi-Task Learning (MTL) based end-toend model has been developed to perform three related tasks: sentence classification depending on the presence of (1) relevant locations and (2) impact information to generate a summary, and (3) identification of the causes or event types in disaster news.Each of the three tasks is formulated as a multilabel binary classification problem.The results of the proposed MTL model have been compared with three popular transformer models: BERT, RoBERTa, and ALBERT.It is observed that the proposed model showed better performance scores than the other models in most cases.